Intro to ML

1. INTRODUCTION
TO
MACHINE
LEARNING
3RD EDITION
ETHEM ALPAYDIN
© The MIT Press, 2014
alpaydin@boun.edu.tr
http://www.cmpe.boun.edu.tr/~ethem/i2ml3e
Lecture Slides for

2. CHAPTER 2:
SUPERVISED
LEARNING

3. Learning a Class from
Examples
3
 Class C of a “family car”
 Prediction: Is car x a family car?
 Knowledge extraction: What do people expect from a
family car?
 Output:
Positive (+) and negative (–) examples
 Input representation:
x1: price, x2 : engine power

4. Training set X
N
t
t
t
,r 1
}
{ 
 x
X




negative
is
if
positive
is
if
x
x
0
1
r
4







2
1
x
x
x

5. Class C
5
   
2
1
2
1 power
engine
AND
price e
e
p
p 




6. Hypothesis class H




negative
is
says
if
positive
is
says
if
)
(
x
x
x
h
h
h
0
1
 
 




N
t
t
t
r
h
h
E
1
1 x
)
|
( X
6
Error of h on H

7. S, G, and the Version Space
7
most specific hypothesis, S
most general hypothesis, G
h H, between S and G is
consistent and make up the
version space
(Mitchell, 1997)

8. Margin
8
 Choose h with largest margin

9. VC Dimension
9
 N points can be labeled in 2N ways as +/–
 H shatters N if there
exists h  H consistent
for any of these:
VC(H ) = N
An axis-aligned rectangle shatters 4 points only !

10. Probably Approximately Correct
(PAC) Learning
10
 How many training examples N should we have, such that with
probability at least 1 ‒ δ, h has error at most ε ?
(Blumer et al., 1989)
 Each strip is at most ε/4
 Pr that we miss a strip 1‒ ε/4
 Pr that N instances miss a strip (1 ‒ ε/4)N
 Pr that N instances miss 4 strips 4(1 ‒ ε/4)N
 4(1 ‒ ε/4)N ≤ δ and (1 ‒ x)≤exp( ‒ x)
 4exp(‒ εN/4) ≤ δ and N ≥ (4/ε)log(4/δ)

11. Noise and Model Complexity
11
Use the simpler one because
 Simpler to use
(lower computational
complexity)
 Easier to train (lower
space complexity)
 Easier to explain
(more interpretable)
 Generalizes better (lower
variance - Occam’s razor)

12. Multiple Classes, Ci i=1,...,K
N
t
t
t
,r 1
}
{ 
 x
X







,
if
if
i
j
r
j
t
i
t
t
i
C
C
x
x
0
1
 







,
if
if
i
j
h
j
t
i
t
t
i
C
C
x
x
x
0
1
12
Train hypotheses
hi(x), i =1,...,K:

13. Regression
  0
1 w
x
w
x
g 

  0
1
2
2 w
x
w
x
w
x
g 


   
 




N
t
t
t
x
g
r
N
g
E
1
2
1
X
|
13
   
 





N
t
t
t
w
x
w
r
N
w
w
E
1
2
0
1
0
1
1
X
|
,
 
  




 
t
t
t
N
t
t
t
x
f
r
r
r
x 1
,
X

14. Model Selection &
Generalization
14
 Learning is an ill-posed problem; data is not
sufficient to find a unique solution
 The need for inductive bias, assumptions
about H
 Generalization: How well a model performs on
new data
 Overfitting: H more complex than C or f
 Underfitting: H less complex than C or f

15. Triple Trade-Off
15
 There is a trade-off between three factors
(Dietterich, 2003):
1. Complexity of H, c (H),
2. Training set size, N,
3. Generalization error, E, on new data
 As N,E
 As c (H),first Eand then E

16. Cross-Validation
16
 To estimate generalization error, we need data
unseen during training. We split the data as
 Training set (50%)
 Validation set (25%)
 Test (publication) set (25%)
 Resampling when there is few data

17. Dimensions of a Supervised
Learner
1. Model:
2. Loss function:
3. Optimization procedure:
 

|
x
g
   
 


t
t
t
g
r
L
E 
 |
,
| x
X
17
 
X
|
min
arg
* 


E
